def step_math(self, dt, J, output, voltage, ref, resources, calcium):
"""Implement the u and x parameters """
x = resources
u = calcium
LIF.step_math(self, dt, J, output, voltage, ref)
#calculate u and x
dx = dt * ((1 - x) / self.tau_x - u * x * output)
du = dt * ((0.2 - u) / self.tau_u + 0.2 * (1 - u) * output)
x += dx
u += du
def test_lif_builtin(rng):
"""Test that the dynamic model approximately matches the rates."""
dt = 1e-3
t_final = 1.0
N = 10
lif = LIF()
gain, bias = lif.gain_bias(rng.uniform(80, 100, size=N), rng.uniform(-1, 1, size=N))
x = np.arange(-2, 2, 0.1)
J = gain * x[:, None] + bias
voltage = np.zeros_like(J)
reftime = np.zeros_like(J)
spikes = np.zeros((int(t_final / dt),) + J.shape)
for i, spikes_i in enumerate(spikes):
lif.step_math(dt, J, spikes_i, voltage, reftime)
math_rates = lif.rates(x, gain, bias)
sim_rates = spikes.mean(0)
assert np.allclose(sim_rates, math_rates, atol=1, rtol=0.02)
def test_lif_builtin(rng):
"""Test that the dynamic model approximately matches the rates."""
dt = 1e-3
t_final = 1.0
N = 10
lif = LIF()
gain, bias = lif.gain_bias(
rng.uniform(80, 100, size=N), rng.uniform(-1, 1, size=N))
x = np.arange(-2, 2, .1)
J = gain * x[:, None] + bias
voltage = np.zeros_like(J)
reftime = np.zeros_like(J)
spikes = np.zeros((int(t_final / dt),) + J.shape)
for i, spikes_i in enumerate(spikes):
lif.step_math(dt, J, spikes_i, voltage, reftime)
math_rates = lif.rates(x, gain, bias)
sim_rates = spikes.mean(0)
assert np.allclose(sim_rates, math_rates, atol=1, rtol=0.02)